Many failures of concrete structures are related to steel corrosion. For this reason, it is important to recognize how the carbonation can affect the durability of reinforced concrete structures. The repeatability of the carbonation depth measure in a specimen of concrete sprayed with a phenolphthalein solution is consistently low whereby it is necessary to have an impartial method to measure the carbonation depth. This study presents two automatic algorithms to detect the non-carbonated zone in concrete specimens. The first algorithm is based solely on digital processing image (DPI), mainly morphological and threshold techniques. The second algorithm is based on artificial intelligence, more specifically on an array of Kohonen networks, but also using some DPI techniques to refine the results. Moreover, another algorithm was developed with the purpose of measure the carbonation depth from the image obtained previously.